U.S. patent number 10,932,714 [Application Number 15/411,633] was granted by the patent office on 2021-03-02 for frequency analysis feedback systems and methods.
This patent grant is currently assigned to Soniphi LLC. The grantee listed for this patent is Soniphi LLC. Invention is credited to Mark Hinds, Matthew Sanderson.
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United States Patent |
10,932,714 |
Sanderson , et al. |
March 2, 2021 |
Frequency analysis feedback systems and methods
Abstract
A health status modulator analyzes frequencies emitted by a
person to select and implement improvement frequencies at the
person. The health status modulator detects frequency information
generated at the person, for example a voice sample or a
vibrational frequency, and determines which significant frequencies
exist within that sample. The modulator could then seek to modify
the person's state my implementing alternative frequencies that
reinforce detected ideal frequencies, introduce missing ideal
frequencies, or counter and eliminate negative frequencies.
Inventors: |
Sanderson; Matthew (Incline
Village, NV), Hinds; Mark (Incline Village, NV) |
Applicant: |
Name |
City |
State |
Country |
Type |
Soniphi LLC |
Incline Village |
NV |
US |
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Assignee: |
Soniphi LLC (Incline Village,
NV)
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Family
ID: |
1000005391635 |
Appl.
No.: |
15/411,633 |
Filed: |
January 20, 2017 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20170202509 A1 |
Jul 20, 2017 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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62281076 |
Jan 20, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61H
23/02 (20130101); A61B 5/4836 (20130101); A61B
7/00 (20130101); A61M 21/02 (20130101); A61N
5/06 (20130101); A61H 1/008 (20130101); A61B
5/486 (20130101); A61B 5/4803 (20130101); A61B
2562/0204 (20130101); A61M 2230/10 (20130101); A61H
2201/10 (20130101); A61M 2230/06 (20130101); A61M
2230/50 (20130101); A61M 2021/0022 (20130101); A61H
2230/105 (20130101); A61B 5/7246 (20130101); A61N
2/00 (20130101); A61M 2021/0027 (20130101); A61H
2201/5092 (20130101); A61M 2021/0044 (20130101); A61H
2230/00 (20130101); A61H 2230/505 (20130101); A61M
2205/50 (20130101); A61H 2201/501 (20130101); A61B
5/726 (20130101); A61M 2230/65 (20130101); A61H
2230/655 (20130101); A61N 1/0456 (20130101); A61N
1/36014 (20130101); A61N 2005/067 (20130101); A61M
2230/10 (20130101); A61M 2230/005 (20130101); A61M
2230/65 (20130101); A61M 2230/005 (20130101); A61M
2230/50 (20130101); A61M 2230/005 (20130101); A61M
2230/06 (20130101); A61M 2230/005 (20130101) |
Current International
Class: |
A61B
5/00 (20060101); A61B 7/00 (20060101); A61M
21/02 (20060101); A61M 21/00 (20060101); A61H
1/00 (20060101); A61H 23/02 (20060101); A61N
5/06 (20060101); A61N 2/00 (20060101); A61N
1/36 (20060101); A61N 5/067 (20060101); A61N
1/04 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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Apr 2010 |
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2014037937 |
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Mar 2014 |
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WO |
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2014037937 |
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Mar 2014 |
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WO |
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2014188408 |
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Nov 2014 |
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WO |
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2015019345 |
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Feb 2015 |
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WO |
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2015187732 |
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Dec 2015 |
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WO |
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2016035069 |
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Mar 2016 |
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WO |
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2016035070 |
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Mar 2016 |
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WO |
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2016035070 |
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Mar 2016 |
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WO |
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2016185460 |
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Nov 2016 |
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WO |
|
Primary Examiner: Dorna; Carrie R
Attorney, Agent or Firm: Fish IP Law, LLP
Parent Case Text
This application claims the benefit of priority to U.S. Patent
Provisional Application No. 62/281,076 filed on Jan. 20, 2016.
These and all other referenced extrinsic materials are incorporated
herein by reference in their entirety. Where a definition or use of
a term in a reference that is incorporated by reference is
inconsistent or contrary to the definition of that term provided
herein, the definition of that term provided herein is deemed to be
controlling.
Claims
What is claimed is:
1. A method for improving a health status of a person comprising:
extrapolating a first set of significant frequencies from a first
set of bio-acoustic information comprising sonic information
embedded within the person's voice; deriving a first set of
correlations based on the first set of significant frequencies;
using at least a portion of the derived first set of correlations
to develop a first protocol that implements a first frequency at a
corresponding first duration; implementing at least a portion of
the first protocol at the person's body; comparing the first set of
significant frequencies against a library of frequencies having a
plurality of frequencies that are related to at least one of
emotion, health status, and physiology; tagging each of the
plurality of frequencies in the library of frequencies as a
positive or negative significant frequency, weighing the tagged
frequencies in the library of frequencies in accordance with an
algorithm; and identifying the first frequency in the first set of
significant frequencies that is the heaviest positively weighed in
the library of frequencies.
2. The method of claim 1, further comprising: extrapolating a
second set of significant frequencies from a second set of
bio-acoustic information comprising sonic information embedded
within the person's voice after implementing the portion of the
first protocol at the person, deriving a second set of correlations
in the second set of frequency information; using at least a
portion of the derived second set of correlations to develop a
second protocol that implements a second frequency at a
corresponding second duration; and implementing at least a portion
of the second protocol at the person's body.
3. The method of claim 2, wherein the step of using the portion of
the derived second set of correlations to develop the second
protocol comprises selecting the second frequency as a function of
a difference between the second set of significant frequencies and
the first set of significant frequencies.
4. The method of claim 2, wherein the second frequency comprises an
alternative frequency to the first frequency, when the first
frequency is not detected in subsequent frequencies collected from
the person within a threshold period of time.
5. The method of claim 1, further comprising receiving the first
set of significant frequencies from a cellular phone.
6. The method of claim 1, further comprising receiving the first
set of significant frequencies from a wearable device.
7. The method of claim 1, wherein the step of deriving the first
set of correlations comprises deriving correlations within a single
wavelet.
8. The method of claim 1, wherein the step of deriving the first
set of correlations comprises deriving correlations between
wavelets.
9. The method of claim 1, further comprising receiving a first set
of health data about the person, wherein deriving the first set of
correlations comprises deriving correlations between the first set
of significant frequencies and the first set of health data.
10. The method of claim 9, wherein the first frequency comprises at
least one of the first set of the significant frequencies.
11. The method of claim 9, wherein the first frequency comprises a
harmonic of at least one of the first set of the significant
frequencies.
12. The method of claim 1, wherein implementing at least a portion
of the first protocol at the person's body comprises reinforcing an
existing significant positive frequency.
13. The method of claim 1, wherein implementing at least a portion
of the first protocol at the person's body comprises introducing a
missing significant positive frequency.
14. The method of claim 1, wherein implementing at least a portion
of the first protocol at the person's body comprises canceling an
existing negative frequency.
15. The method of claim 1, wherein extrapolating the first set of
significant frequencies comprises emitting frequencies at the
person and detecting frequency feedback from the person's body.
16. A method for improving a health status of a person comprising:
extrapolating a first set of significant frequencies from a first
set of bio-acoustic information comprising sonic information
embedded within the person's voice; deriving a first set of
correlations based on the first set of significant frequencies;
using at least a portion of the derived first set of correlations
to develop a first protocol that implements a first frequency at a
corresponding first duration; implementing at least a portion of
the first protocol at the person's body; wherein the step of
extrapolating the first set of significant frequencies from the
first set of bio-acoustic information comprises identifying
frequencies that appear more than five times in at least 80% of a
contiguous portion of the bio-acoustic information.
17. The method of claim 16, further comprising: extrapolating a
second set of significant frequencies from a second set of
bio-acoustic information comprising sonic information embedded
within the person's voice after implementing the portion of the
first protocol at the person, deriving a second set of correlations
in the second set of frequency information; using at least a
portion of the derived second set of correlations to develop a
second protocol that implements a second frequency at a
corresponding second duration; and implementing at least a portion
of the second protocol at the person's body.
18. The method of claim 16, wherein the step of deriving the first
set of correlations comprises deriving correlations within a single
wavelet.
19. The method of claim 16, wherein the step of deriving the first
set of correlations comprises deriving correlations between
wavelets.
20. The method of claim 16, further comprising receiving a first
set of health data about the person, wherein deriving the first set
of correlations comprises deriving correlations between the first
set of significant frequencies and the first set of health data.
Description
FIELD OF THE INVENTION
The field of the invention is wavelet analysis of vocal
samples.
BACKGROUND
The following description includes information that may be useful
in understanding the present invention. It is not an admission that
any of the information provided herein is prior art or relevant to
the presently claimed invention, or that any publication
specifically or implicitly referenced is prior art.
All publications identified herein are incorporated by reference to
the same extent as if each individual publication or patent
application were specifically and individually indicated to be
incorporated by reference. Where a definition or use of a term in
an incorporated reference is inconsistent or contrary to the
definition of that term provided herein, the definition of that
term provided herein applies and the definition of that term in the
reference does not apply.
Automatically diagnosing the state of a living entity using
electronic devices is difficult without bulky machinery, for
example an x-ray machine or an ultrasound machine. While portable
diagnosis machinery exists, such machinery is typically quite
expensive as it requires specialized hardware, such as a radiation
emitter or an ultrasound emitter and a sonograph.
For example, U.S. Pat. No. 7,520,861 to Murphy teaches a lung sound
diagnostic system that collects, organizes, and analyzes lung
sounds associated with inspiration and expiration of a patient.
Murphy's system uses transducers that are placed at various sites
around the patient's chest, which are coupled to signal processing
circuitry that digitizes the data and transmits the data to a
computer station. Murphy's system, requires expensive, specialized
hardware in an environment that needs a great deal of advance
notice to set up. Murphy's system also merely diagnoses the state
of the patient and fails to provide any sort of treatment.
U.S. Pat. No. 8,078,470 to Levanon teaches a system that analyzes
intonation of a speaker to determine the emotional attitude of the
speaker. Levanon's emotional attitude system records and digitizes
a word spoken by the speaker, and processes the digital signal to
determine the average frequency of the speaker, and compares that
average frequency to reference frequencies to determine the
speaker's emotional state. Levanon's emotional attitude system is
easy to set up with commonly available computer devices since it
only requires a voice recorder and a computer. Levanon's emotional
attitude system, however, only identifies the speaker's emotional
state, and fails to provide any way to alter the speaker's state in
any manner.
US WO 2014/188408 to Levanon teaches a diagnosis system that
detects a multisystem failure in a patient by analyzing the
patient's speech. Levanon's diagnosis system calculates an
intensity of the patient's speech across a plurality of
frequencies, and determines whether the patient is suffering from a
multisystem failure by the number of vibrations found in a portion
of the patient's speech. Levanon's diagnosis system may be used to
detect a patient's multisystem failure, but fails to provide any
way to alter the patient's state in any manner.
Thus, there is still a need for systems and methods to diagnose the
state of a living entity and alter the state using commonly
available hardware.
SUMMARY OF THE INVENTION
The inventive subject matter provides apparatus, systems, and
methods in which a health status modulator analyzes frequencies
emitted by a person to select and implement improvement frequencies
at the person. The system could use any suitable frequency
information to derive the health of the person, for example
bio-acoustic information, bio-electronic information (e.g.
electromagnetic frequencies, heart-rate frequencies, galvantic skin
response frequencies), bio-magnetic information, bio-vibrational
information, and bio-luminescent information (light frequencies).
As used herein, "bio-acoustic information" comprises sonic
information embedded within a voice sample--excluding linguistic
data. As used herein, "linguistic data" comprises any information
that requires knowledge of a language to decipher and/or
understand, such as English, Russian, or Mandarin Chinese. As used
herein, "bio-electronic information" comprises electronic impulses,
such as current, voltage, and frequency, emanating from a person.
As used herein, "bio-magnetic information" comprises any magnetic
fields detected from a person. As used herein, "bio-vibrational
information" comprises any tactile vibrations detected upon a
surface of a person or upon a surface of clothing worn by the
person. As used herein, "bio-luminescent information" comprises
light waves reflecting off of a surface of the person. Preferably,
the system uses the frequency information to develop a protocol
that implements a frequency for a duration of time at the person.
As used herein, "at the person" means within two meter's distance
from a center of the person, and more preferably within 1.5 meter's
distance from a center of the person, within 1 meter's distance
from a center of the person, or even within 0.5 meter's distance
from the center of the person.
Devices located "at the person" could be worn by the person, be
placed within a pocket worn by the person, could be embedded within
a body part of the person, or could be placed within a proximate
area of the person. Any suitable computer system device could be
used, for example a desktop computer system or a mobile computer
system (e.g. laptop, mobile phone). An application could be
installed on any computer system having a frequency sensor to
enable that computer system to collect frequency information from
the person.
The system can collect frequency information from the person in a
variety of ways. In some embodiments, the system collects passive
emitted frequency data, such as bio-acoustic information via a
person speaking into a microphone or heart rate information via a
person wearing an electro dermal device. In systems that collect
bio-acoustic information, the system could record one or more voice
samples that contain bio-acoustic information emitted by the
person's voice. The system could collect one or more voice samples
actively, for example in response to the person activating a
trigger via a user interface, or could collect the voice sample
passively by monitoring sounds emitted by the person. In some
embodiments, the system could be initialized to recognize the
person's voice via a speech recognition algorithm. Once the system
has been initialized, the system could analyze sounds and filter
out ambient noise that is not recognized as originating from the
person. In some embodiments, the system is programmed to collect
mel-frequency cepstrum coefficients (MFCC) from the bio-acoustic
information on a mel scale.
In other embodiments the system emits frequencies at the person,
such as a laser aimed at portions of the person's body at a
frequency or an electrode that transmits electronic signals through
the person's body, and detects frequency feedback from the person's
body similar to a radar "pinging" portions of the person's body. In
systems that collect bio-electronic information, the system could
record electronic impulses detected through an electrodermal
sensor. In some embodiments, the system implements a frequency
sweep of a part of the person's body to derive the strength of
resonant frequencies.
Frequency information could be collected by a sensor at the person,
for example a microphone embedded in a cellular phone or an
electronic wearable device functionally coupled to a computer
system, which transmits frequencies to a centralized computer
system for analysis. In some embodiments, the sensor could be
surgically implanted within the person's body, such as within a
pacemaker or other implantable device, which transmits detected
frequencies to a computer system functionally coupled to the
sensor. As used herein, an electronic device that is "functionally
coupled" to another electronic device is coupled in such a way as
to allow electronic data to be transmitted from one electronic
device to another electronic device, using a wired or wireless data
connection. Contemplated sensors include microphones,
electroencephalograms, electrodermal sensors, cameras, infrared
sensors, and antennas. The frequency information could be a sample
over any period of time suitable to collect enough information to
derive a set of frequency data, for example at most 2 seconds, at
most 5 seconds, at most 10 seconds, at most 30 seconds, at most 1
minute, or even at most 5 minutes. In some embodiments, a user
interface might be presented to the person, triggering the person
to perform an activity that would cause frequencies of the person
to be easier to capture, such as placing electrodermal sensors on a
portion of the person's body, or read a sentence presented on the
user interface into a microphone sensor. The sensor could be
configured to transmit either the raw data to a remote computer
system, or could be configured to transmit only derived frequency
information (e.g. bio-acoustic information, bio-electronic
information, bio-magnetic information, bio-vibrational information,
or bio-luminescent information) to a remote computer system distal
from the person for processing.
Frequency information extracted from the collected raw sensor data
is typically transmitted to a frequency processing module to be
analyzed. In preferred embodiments, the frequency information is
analyzed by a computerized frequency processing module which
derives frequency information from the collected raw data from the
sensor or sensors at the person. Preferably, a full spectral
analysis of the raw data is performed in order to extract as much
frequency information as possible from the raw data. Exemplary
frequency information includes, for example, a highest dB (decibel)
reading, a lowest dB reading, cumulative octave readings,
harmonics, and logical groupings of frequencies. In some
embodiments, the frequency processing module could be configured to
derive one or more significant frequencies from the raw data. As
used herein, a "significant frequency" comprises a recurring (more
than 5 times) frequency that can be detected in at least 80% of a
contiguous portion of the received frequency information.
Once one or more "significant frequencies" are identified, the
significant frequencies could be fed to a frequency analysis
module, which compares the significant frequencies against a
library of frequencies that relate to emotions (happy, sad, angry,
stressed), health status, and physiology (toxicity, nutrient level,
hormonal imbalances). The library of frequencies can be pulled from
any suitable source, for example a communal library (shared by all
users of the system), a master library (created by administrators
of the system), or a personal library (created by a user or subset
of users of the system). Personal libraries could be created and
maintained by a user or a subset of users who record frequencies
when the user has an emotional state, a physiological condition, or
a health status. The system could then record and save significant
frequencies detected during that recording and store that
significant frequency in the library to identify when the user (or
set of users) is emitting that significant frequency, which
reflects that state.
The frequency library could be tagged with positive and negative
significant frequencies, which and could be weighted in accordance
with any suitable algorithm, for example an automated template that
chooses optimal frequencies for selected user types (e.g. an
athlete user type may have a first set of weighted significant
positive/frequencies while an accountant user type may have a
different set of weighted significant positive/frequencies). The
system could then identify which of the detected significant
frequencies are weighted the most, and implement a protocol to
alter a state of the person. For example, the system could
reinforce an existing significant positive frequency, introduce a
missing significant positive frequency that is over a threshold
weight, or could cancel an existing negative frequency. The system
is programmed to address the highest weighted frequencies (or
lowest weighted frequencies, in the case of a negative weight for
negative frequencies) when reinforcing, introducing, or canceling a
significant frequency.
Typically analysis module develops the protocol as a function of a
portion of the frequency information. As used herein, a protocol
that "implements" a frequency at a duration is one that instructs a
device to resonate at the frequency for the duration specified. A
protocol could be configured to implement a plurality of
frequencies at a plurality of durations if need be. Such
frequencies could be implemented using any suitable device that
could be directed to resonate at a frequency, for example an audio
speaker, a laser, a light source, a pulsed electromagnetic field
(PEMF) device, a SCALAR wave device, a transcutaneous electrical
nerve stimulation (TENS) device, a microcurrent electrical nerve
stimulation (MENS) device, or a vibrational motor that transmits a
tactilely sensible vibrational frequency. In some embodiments, the
system could construct a protocol to implement a weakly detected
frequency in the bio-acoustic information. In simple embodiments,
the system could construct a protocol to implement the significant
frequency for the period of time that the voice sample was
recorded. The system could also construct a protocol to implement a
harmonic of the significant frequency, multiple harmonics of the
significant frequency, or could implement the significant frequency
via different modalities (e.g. via an audio sound and also a visual
light). In some embodiments, the protocol could implement the
frequency by aiming the frequency at a portion of the person's
body, for example the person's ears, eyes, nose, throat, chest, or
hips. In other embodiments, the protocol could implement the
frequency by aiming the frequency at multiple portions of the
person's body, and could implement different frequencies at
different portions of the person's body (e.g. directing the
significant frequency towards the person's ears, and a harmonic of
the significant frequency towards the person's diaphragm). Where a
plurality of frequencies are directed at a person, each frequency
could be implemented at a different duration and/or duty cycle.
The system could receive several sets of frequency information from
a person, for example through several samples of data collected
from the sensors one after another (e.g. within 5 minutes of one
another) or through several historical samples of data submitted
over time and saved to an archived database (e.g. one week, one
month, or even one year after one another). Several protocols could
be developed, one for each set of frequency information, and/or
each type of frequency information. In some embodiments, the system
could be configured to compare a first set of frequency information
with a second set of frequency information in order to develop a
follow-up protocol. For example, where the system is configured to
strengthen a significant frequency of a person, the system could
detect a decibel level of the person's significant frequency in
accordance with the first set of frequency information, and the
decibel level of the person's significant frequency in accordance
with the second set of frequency information, and could
increase/decrease the intensity of the implemented frequency
depending upon if the significant frequency decreased/increased in
decibel level, respectively. In some embodiments, the system could
be configured to save the received frequency information to a
database to provide a historical frequency map of the person. Such
historical frequency maps could be used to develop person-specific
protocols.
For example, the system could determine that the person regains an
intensity in voice samples or frequency feedback when a first
frequency is implemented at the person, but fails to regain an
intensity (or does not gain as large an intensity) when a second
frequency is implemented at the person. The system could then favor
implementing the first frequency at the person when such an
analysis is performed. In some embodiments, the system could save
the raw frequency information into the database, but preferably the
system only saves historical analysis information to the database
to save space. Exemplary analysis information includes a
significant frequency of the person, a set of harmonic frequencies
that are known to strengthen the significant frequency of the
person, the highest recorded decibel frequency, the lowest recorded
frequency, the types of frequency recorded and implemented at the
person, and a preferred significant frequency of the person. The
system could save the frequency information in a variety of ways,
for example by molecular weight and frequency correlations, by
genetic code and wavelength correlations, and/or as light emission
spectral analysis data.
Once one or more protocols have been developed by the analysis
module, the system could feed the protocol to a computerized
effector transmitter that transmits the treatment frequency to a
frequency implementer for a corresponding duration at the person.
Contemplated frequency implementers include any device that can
implement a frequency, such as an audio speaker, a light source
(e.g. an LED or laser), a vibrational source, a microcurrent
emitter, a PEMF device, and a SCALAR wave device. In some
embodiments, the frequency implementer modifies the frequency of an
environment about the person, such as a home entertainment system
or a car entertainment system, and in other embodiments the
frequency implementer modifies the frequency by coupling to the
person, for example via headphones, a bracelet, or a head-band, and
emits frequencies in any modality--even through bone conductions
that deliver frequency through one or more organs of the body (e.g.
jawbone to ear canal). In preferred embodiments, a laser or other
aiming device could be used for extreme targeting of a person or a
portion of a person's body. Both targeted bands and untargeted
bands could be used. The frequencies could be implemented in a
single phase, biphasic, or in multiple phases, and alternating
frequencies could be implemented (e.g. a first frequency then a
second frequency and then the first frequency again, or a first,
second, then third frequency followed by the first frequency
again). Such frequency implementers could implement the protocol at
the person in order to affect the health status of the person, for
example by reinforcing, introducing, or cancelling a frequency.
Various objects, features, aspects and advantages of the inventive
subject matter will become more apparent from the following
detailed description of preferred embodiments, along with the
accompanying drawing figures in which like numerals represent like
components. For example, instead of implementing frequencies at the
person, the system could be configured to implement the frequency
into food or water, which could then be ingested by the person. In
other embodiments, the system could be configured to implement the
frequency into an ingestible medium or into a wearable medium (e.g.
a quartz crystal), which is then transported to the person for
wearing.
Various objects, features, aspects and advantages of the inventive
subject matter will become more apparent from the following
detailed description of preferred embodiments, along with the
accompanying drawing figures in which like numerals represent like
components.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is an exemplary system distributed on a computer system and
a portable device at the person
FIG. 2 is a software schematic of an exemplary computer system.
FIG. 3 is a flowchart of steps to monitor and effect the health
status of a person.
DETAILED DESCRIPTION
As used in the description herein and throughout the claims that
follow, the meaning of "a," "an," and "the" includes plural
reference unless the context clearly dictates otherwise. Also, as
used in the description herein, the meaning of "in" includes "in"
and "on" unless the context clearly dictates otherwise.
Unless the context dictates the contrary, all ranges set forth
herein should be interpreted as being inclusive of their endpoints,
and open-ended ranges should be interpreted to include only
commercially practical values. Similarly, all lists of values
should be considered as inclusive of intermediate values unless the
context indicates the contrary.
The recitation of ranges of values herein is merely intended to
serve as a shorthand method of referring individually to each
separate value falling within the range. Unless otherwise indicated
herein, each individual value with a range is incorporated into the
specification as if it were individually recited herein. All
methods described herein can be performed in any suitable order
unless otherwise indicated herein or otherwise clearly contradicted
by context. The use of any and all examples, or exemplary language
(e.g. "such as") provided with respect to certain embodiments
herein is intended merely to better illuminate the invention and
does not pose a limitation on the scope of the invention otherwise
claimed. No language in the specification should be construed as
indicating any non-claimed element essential to the practice of the
invention.
Groupings of alternative elements or embodiments of the invention
disclosed herein are not to be construed as limitations. Each group
member can be referred to and claimed individually or in any
combination with other members of the group or other elements found
herein. One or more members of a group can be included in, or
deleted from, a group for reasons of convenience and/or
patentability. When any such inclusion or deletion occurs, the
specification is herein deemed to contain the group as modified
thus fulfilling the written description of all Markush groups used
in the appended claims.
Throughout the following discussion, numerous references will be
made regarding servers, services, interfaces, portals, platforms,
or other systems formed from computing devices. It should be
appreciated that the use of such terms is deemed to represent one
or more computing devices having at least one processor configured
to execute software instructions stored on a computer readable
tangible, non-transitory medium. For example, a server can include
one or more computers operating as a web server, database server,
or other type of computer server in a manner to fulfill described
roles, responsibilities, or functions. One should appreciate that
the systems disclosed herein can be used to detect one or more
significant frequencies at a person and reinforce existing
significant frequencies, introduce missing significant frequencies,
and/or cancel existing significant frequencies.
The following discussion provides many example embodiments of the
inventive subject matter. Although each embodiment represents a
single combination of inventive elements, the inventive subject
matter is considered to include all possible combinations of the
disclosed elements. Thus if one embodiment comprises elements A, B,
and C, and a second embodiment comprises elements B and D, then the
inventive subject matter is also considered to include other
remaining combinations of A, B, C, or D, even if not explicitly
disclosed.
As used herein, and unless the context dictates otherwise, the term
"coupled to" is intended to include both direct coupling (in which
two elements that are coupled to each other contact each other) and
indirect coupling (in which at least one additional element is
located between the two elements). Therefore, the terms "coupled
to" and "coupled with" are used synonymously.
In FIG. 1, a system 100 includes an analysis computer system 110, a
network 120, a control computer system 130, control computer system
140, control computer system 152, control computer system 154,
control computer system 156, and a person 160.
Analysis computer system 110 is shown euphemistically as a single
computer tower having a processor and a non-transient memory with
software configured to perform analysis and protocol development on
a voice sample or a set of frequency information, but analysis
computer system 110 could be distributed among a plurality of
computers, or could be implemented on a network cloud without
departing from the scope of the current invention. Data repository
112 is functionally coupled to computer system 110 and stores data
collected and/or analyzed by analysis computer system 110, such as
frequency data, health status reports, profile data for one or more
users of the system, and/or preferences. Such data sources
typically store collected information in a text file, such as a
log, csv, JSON or an XML file. Data repository 112 could be a
database management system ("DBMS"), which keeps data in a
structured environment, and typically keeps metadata log files on
its datasets. While data repository 112 is shown euphemistically as
a single data repository, any number of data sources and any type
of data source could be used without departing from the scope of
the invention. The data sources coupled to computer 110 could
number in the hundreds or even thousands, to provide a large corpus
of datasets that may or may not be known to computer system 110,
where many of the data sources might use different types of data
structures. Both analysis computer system 110 and data repository
112 could be distributed systems in a cloud computer environment.
Data repository 112 could also be considered a data source having
one or more datasets that analysis computer system 110 could draw
upon. Data repository 112 could also contain a historical log that
tracks all retrieving, profiling, querying and conforming of
datasets, attributes of datasets, and associated user entity
interactions to enable the system to learn from itself by analyzing
trends found in the historical log.
Network 120 could be any network link that is used to communicate
data from one computer system to another computer system, such as a
packet-switched network, the Internet, LAN, WAN, VPN, or other
suitable network system. Analysis computer system 110 communicates
with various control communication systems via network 120 to
transmit frequency information and frequency implementation
protocols between the various devices.
Control computer systems 130, 152, 154, and 156 are shown
euphemistically as mobile devices, but could be any computer system
programmed to collect frequency information from one or more users,
for example a wearable computer device (e.g. a badge, a pin, a
button, a cufflink, a watch, a bracelet, a necklace, an elbow pad,
or a piece of clothing), an implantable device, or could be coupled
to a portion of a skin of person 140, such as a bracelet, a belt,
or an electrodermal heart rate monitor. Control system 130 is
functionally coupled to devices 132, 134, 136, and 138, which
function to collect frequency information from person 160 and/or
implement frequencies at person 160. For example, device 132 is
shown as a microphone that collects audio frequency information,
device 134 is shown as a headset that could collect vibrational
frequency from person 160 and deliver vibrational frequency and/or
audio frequencies at person 160, device 136 is shown as a wristband
that could implement vibrational or electrical frequencies at
person 160, and speaker 138 is shown as a speaker that could
implement audio frequencies at person 160. Any device suitable for
collecting frequency information or for delivering frequency
information in any modality is contemplated. Contemplated
modalities include, for example, audio modalities, light
modalities, vibrational modalities, magnetic modalities, SCALAR
modalities, electrical modalities, and radio frequency modalities.
While control computer system 130 could be physically coupled to
each device 132, 134, 136, and 138, control computer system 130
could be functionally coupled to each device through wireless means
as well.
Contemplated frequency data collectors include any suitable device
that could be used to collect frequency information from person
160, for example an electrodermal sensor, electroencephalogram,
camera, infrared sensor, or antenna. As used herein, a "frequency
dataset" is a dataset that contains oscillating wave data collected
by a sensor. One or more sensors could be implanted within person
160, but is preferably wearable, placed in a pocket, or is coupled
to a portion of person 160's skin, such as a bracelet or a belt. In
some embodiments, the frequency data collectors collect frequency
information passively, for example by constantly collecting audio
and vibrational samples from person 160, but in preferred
embodiments the frequency data collectors collect frequency
information in response to some sort of trigger, for example a
trigger word uttered by person 160 or a button activated on control
system 130. In some embodiments, control computer system 130 could
transform the raw collected frequency datasets into a subset of
frequency information, for example by gleaning only bioacoustic
data from a voice sample and transmitting only the bioacoustic data
to analysis computer system 110. However in other embodiments
control computer system 130 could be configured to transmit raw
frequency datasets to analysis computer system 110.
Frequency information could also include wavelets. In signal
processing a wavelet is an oscillation that begins and ends at zero
amplitude, with an initial increase, a peak then decreasing until
its amplitude is zero. A wavelet is created using a wavelet
transform which is similar to other transforms that can transform a
signal from the time domain into the frequency domain, the wavelet
transform however contains both the information of the time domain
and the frequency domain with the Heisenberg uncertainty principle
effecting its accuracy at various ranges. The wavelet can be used
in signal processing to identify when a certain frequency is
present in time with regards to a signal of duration N.
In the application of signal processing of discreet vocal data, the
creation of distinct wavelets of single frequencies can be
convolved with sample signals of the human voice to ascertain
information that would show correlation between the created
wavelets and the sample signal. These correlations can be of value
as biometric information specific to the unique vocal print of the
person whose sample was used to generate the signal.
Analysis computer system 110 could use wavelet analysis to identify
unique spectral data present in the human voice as well as
background noise present in a sample recorded signal obtained from
a mobile device or stationary microphone. Analysis computer system
110 could convolve wavelets created at distinct frequencies with
unknown sample signals to find correlations between the wavelet and
the unknown signal. Through these wavelet correlations, the present
invention can determine biometric data about the person whose voice
was sampled, including but not limited to; bio-specific
identifiers, possible biochemical presence, phase information,
harmonic resonance, dissonance, and coherence of the vocal signal.
When the results of these correlations are compared to the many
databases that represent the bulk of the present invention's
intellectual property, very specific correlations to a person's
unique vocal profile can be garnered, and a general picture of the
person's personality and health and wellness can be achieved.
An audio sensor is preferably configured to collect audio
information from the person, such as a microphone coupled to a
computer system that collects snippets of audio data, such as a 30
second, 60 second, 5 minute, or even hour long sample. Preferably,
the system analyzes frequency data in the audio data and identifies
quantifiable, correlative trends. For example, the system could
identify correlations within the frequency data (e.g. a significant
frequency or a highest increase in frequency and a highest decrease
in frequency), correlations between wavelets in the audio sample
(e.g. correlations between fundamental frequencies or harmonics),
correlations between wavelets between audio samples, or even
correlations between attributes of a wavelet and health status of
that person (e.g. how wavelet attributes correlate with a disease
state, a state of mind, and how those attributes change when
wavelet attributes change). Such correlations could be saved to
database 112 and used to determine information about the person in
the future, for example the system could analyze a person's wavelet
information and determine the health status of that person, or
could analyze a person's wavelet information and identify the
person via a saved wavelet "fingerprint" in the database.
Frequency implementers include any suitable device that could be
used to implement a frequency at person 160, for example a laser, a
light source, a pulsed electromagnetic field (PEMF) device, a
SCALAR wave device, a transcutaneous electrical nerve stimulation
(TENS) device, a microcurrent electrical nerve stimulation (MENS)
device, or a vibrational motor that transmits a tactilely sensible
vibrational frequency. Frequency implementers are configured to
receive a frequency protocol and implement one or more frequencies
at person 160 in accordance with the frequency protocol (e.g. a
first frequency for a first time period, followed by as second
frequency for a second time period, and so on and so forth).
Multiple frequencies could be implemented at person 160
simultaneously, and the frequency data collectors could collect
frequency data during implementation, transmitting that frequency
data to analysis computer system 110 so that it can alter or fix
the implemented protocol as needed. For example, where a first
frequency is introduced to person 160, and person 160 fails to
provide frequencies that reflect that frequency, computer system
110 could introduce a protocol that increases the intensity of that
frequency, introduces a harmonic of that frequency, or stop
introducing that frequency and provide an alternative frequency
(e.g. a frequency associated with joy at a higher weight is
introduced, but was not detected in subsequent frequencies
collected from person 160 within a threshold period of time, so a
frequency associated with relaxation at a lower weight is
introduced).
Control computer systems 152, 154, and 156 are shown as other
control computer systems that collect frequency information and/or
implement frequencies at other persons (not shown). Control
computer systems 152, 154, and 156 are shown as mobile phones, but
could be any other computer system capable of collecting frequency
information and/or implementing frequencies.
Analysis computer system 110 or any of the control computer systems
130, 152, 154, or 156, could be programmed to derive significant
frequencies from the collected frequency information. In
embodiments where the control computer systems be programmed to
derive significant frequencies, only the significant frequency
information could be transmitted to analysis computer system 110.
In preferred embodiments, the frequency information is analyzed by
a computerized frequency processing module which derives frequency
information from the frequency dataset(s). Preferably, a full
spectral analysis of the frequency dataset(s) is performed in order
to extract as much non-linguistic frequency information as
possible. Exemplary significant frequency information includes, for
example, a highest dB (decibel) reading, a lowest dB reading,
cumulative octave readings, harmonics, logical groupings of
frequencies, and statistically significant frequencies as compared
to other detected frequencies in the frequency information. In
other embodiments, the frequency processing module could derive the
significant frequency to be the strongest frequency detected within
a portion of the frequency feedback sample, or the strongest
whole-number frequency detected within a portion of the frequency
feedback sample.
FIG. 2 shows a software schematic of modules within an analysis
computer system 220, such as analysis computer system 110. Analysis
computer system 220 communicates with one or more control modules
and has a frequency processing module 220, frequency analysis
module 230, and an effector transmitter 240.
Frequency processing module 220 receives frequency information from
one or more control modules, such as control module 210, and parses
out the significant frequencies (if the control module has not done
so already). The significant frequencies are then transmitted to
frequency analysis module 230, which detects the health state of
the person by correlating the detected significant frequencies in
the frequency data with historical frequencies saved in frequency
database 232. Analysis computer system 220 stores frequency data in
frequency database 232 that could be utilized by analysis module
230 to make correlations. The frequency data could comprise various
correlations between frequencies of modalities and health statuses,
such as emotional state, health state, or physiology. Any user of
the system could provide additional frequency data gleaned from
his/her own self, or from other frequency data archives. Such users
include a user of a control module, and administrator user, or a
content aggregator.
Frequency database 232 could house several sets of frequency
information from one or more persons, for example through several
samples of data collected from the sensors one after another (e.g.
within 5 minutes of one another) or through several historical
samples of data submitted over time and saved to an archived
database (e.g. one week, one month, or even one year after one
another). Frequency analysis module 230 could then compare the
received frequency dataset information against historical frequency
dataset information from the person, or from other persons with
similar characteristics. The similar characteristics could be
selected through an administrator user interface. For example, a
user could wish to compare the frequency feedback dataset against
frequency characteristics of other users who have the same racial
background, the same age and sex, and/or the same profession. In
some embodiments, a user could compare his/her own frequency
feedback information against a selected ideal frequency
dataset.
A user of the system could provide any algorithm for selecting a
suitable protocol when a correlation is detected. For example,
frequency analysis module could utilize an algorithm that detects
whether at least one of a set of positive significant frequencies
was detected, and if none of that set were detected, implement the
heaviest weighted significant frequency of the set of positive
significant frequencies. In another embodiment, frequency analysis
module could utilize an algorithm that detects whether at least one
of a set of positive significant frequencies was detected, and if
none of that set were detected, determine a difference between a
detected significant frequency and an ideal significant frequency,
and introduce another frequency in phase with the detected
significant frequency that aggregates with the detected significant
frequency to produce the ideal significant frequency. In another
embodiment, the frequency analysis module could utilize an
algorithm that detects whether at least one of a set of positive
significant frequencies was detected, and if one of that set is
detected, reinforce that significant frequency. In another
embodiment, frequency analysis module could utilize an algorithm
that detects whether at least one of a set of negative significant
frequencies was detected, and if one of that set is detected,
implement an opposing frequency at the person to cancel out the
negative significant frequency. It should be apparent to those
skilled in the art that many more combinations and algorithms
besides those already described are possible. Maintenance
algorithms could also be implemented to implement alternative
protocols where a first protocol fails to prove effective (i.e. the
change in the person's detected significant frequencies falls below
a threshold level).
Frequency analysis module 230 chooses a protocol that implements a
frequency at a corresponding duration. Typically the frequency
information is fed into an effector transmitter that transmits the
protocol to a frequency implementer, either directly such as a
transmission to frequency emitter 250, or indirectly through a
control module functionally coupled to a frequency emitter, such as
control module 210. As used herein, a protocol that "implements" a
frequency at a duration is one that instructs a device to resonate
at the frequency for the duration specified. A protocol could
implement one or more frequencies at one or more durations at one
or more modalities if need be. Such frequencies could be
implemented using any suitable device that could be directed to
resonate at a frequency, for example an audio speaker, a laser, a
light source, a pulsed electromagnetic field (PEMF) device, a
SCALAR wave frequency, or a vibrational motor that transmits a
tactilely sensible vibrational frequency.
The system could also construct a protocol to implement a harmonic
of the significant frequency, multiple harmonics of the significant
frequency, or could implement the significant frequency via
different modalities (e.g. via an audio sound and also a visual
light). In some embodiments, the protocol could implement the
frequency by aiming the frequency at a portion of the person's
body, for example the person's ears, eyes, nose, throat, chest, or
hips. In other embodiments, the protocol could implement the
frequency by aiming the frequency at multiple portions of the
person's body, and could implement different frequencies at
different portions of the person's body (e.g. directing the
significant frequency towards the person's ears, and a harmonic of
the significant frequency towards the person's diaphragm). Where a
plurality of frequencies are directed at a person, each frequency
could be implemented at a different duration, phase, and/or duty
cycle.
FIG. 3 shows an exemplary method for analyzing detected frequencies
and implementing improvement frequencies. In step 310, the system
receives a first set of frequency information generated by a person
from a set of frequency sensors. In step 320, the system
extrapolates a first set of significant frequencies from the first
set of frequency information, and derives a set of correlations
based on the set of significant frequencies. As used herein, a
"set" of items includes at least one item. The correlations are
then used to develop a protocol in step 340, which are then
implemented at the person using any suitable frequency
implementer.
Preferably, a feedback loop is also implemented, such that
additional frequency information is collected in step 315. Again,
the system runs through similar steps of extrapolating an updated
set of significant frequencies in step 325, and deriving
correlations based on the significant frequencies in step 335. In
step 345, the system compares the updated derived correlations
against historical derived correlations to determine how effective
the previous protocol was at effecting change at the person, and
could then implement a second protocol as a function of those
correlations. In step 355 the system then implements a the second
protocol at the person using one or more frequency implementers.
The feedback loop could be continued for any period of time for a
user of the system.
It should be apparent to those skilled in the art that many more
modifications besides those already described are possible without
departing from the inventive concepts herein. The inventive subject
matter, therefore, is not to be restricted except in the spirit of
the appended claims. Moreover, in interpreting both the
specification and the claims, all terms should be interpreted in
the broadest possible manner consistent with the context. In
particular, the terms "comprises" and "comprising" should be
interpreted as referring to elements, components, or steps in a
non-exclusive manner, indicating that the referenced elements,
components, or steps may be present, or utilized, or combined with
other elements, components, or steps that are not expressly
referenced. Where the specification claims refers to at least one
of something selected from the group consisting of A, B, C . . .
and N, the text should be interpreted as requiring only one element
from the group, not A plus N, or B plus N, etc.
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